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--- |
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title: Hand Written Text Digit Recognition |
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emoji: 📉 |
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colorFrom: blue |
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colorTo: red |
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sdk: streamlit |
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sdk_version: 1.28.1 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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# Task : Handwritten Text Digit Recognition using streamlit |
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## Problem Statement: |
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Handwritten digit recognition is a fundamental task in machine learning and computer vision that involves identifying and classifying individual digits (0-9) that have been handwritten. This task plays a crucial role in various applications across different domains. |
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This project aims to develop a handwritten digit recognition system using deep learning techniques. It involves building and training models that can accurately classify handwritten digits. |
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## Importance and Use Cases: |
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1. Finance: Automatic check reading, where handwritten numbers on checks are recognized to process financial transactions efficiently. |
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2. Postal Services: ZIP code recognition, aiding postal services in sorting and delivering mail accurately. |
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3. Data Entry and Forms: Digit extraction in forms, simplifying data entry processes and reducing errors in handwritten information. |
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4. Educational Tools: Handwritten digit recognition is also used in educational tools to provide interactive learning experiences, such as math and number games. |
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5. OCR Systems: It is a crucial component of Optical Character Recognition (OCR) systems used for digitizing printed and handwritten text. |
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## Challenges: |
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Handwritten digit recognition presents several challenges, including variations in writing styles, different writing tools, noise in scanned images, and variations in digit orientation and size. |
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## Overview |
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This project aims to implement a Handwritten Digit Recognition system. We have trained a machine learning model and loaded the model weights (model_weights.pth). By running the app.py script, you can interact with the application. |
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## Usage Instructions |
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Follow these steps to use the application: |
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1. **Installation**: Ensure you have installed all the required libraries and packages. |
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2. **Model Setup**: We've trained a model and saved its weights in model_weights.pth. Make sure you have this file in your project directory. |
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3. **Run the App**: Execute the app.py script to launch the interactive application. |
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4. **Select Colors**: Use the application to choose the background color and stroke color. You can also load an input image or draw a digit. |
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5. **Digit Prediction**: The model will predict the drawn digit or the digit in the loaded image. |
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Enjoy using the Handwritten Digit Recognition app! |
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